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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Rapid Aeroelastic Analysis of Blade Flutter in Turbomachines

The LINFLUX-AE computer code predicts flutter and forced responses of blades and vanes in turbomachines under subsonic, transonic, and supersonic flow conditions. The code solves the Euler equations of unsteady flow in a blade passage under the assumption that the blades vibrate harmonically at small amplitudes. The steady-state nonlinear Euler equations are solved by a separate program, then equations for unsteady flow components are obtained through linearization around the steady-state solution. A structural-dynamics analysis (see figure) is performed to determine the frequencies and mode shapes of blade vibrations, a preprocessor interpolates mode shapes from the structural-dynamics mesh onto the LINFLUX computational-fluid-dynamics mesh, and an interface code is used to convert the steady-state flow solution to a form required by LINFLUX. Then LINFLUX solves the linearized equations in the frequency domain to calculate the unsteady aerodynamic pressure distribution for a given vibration mode, frequency, and interblade phase angle. A post-processor uses the unsteady pressures to calculate generalized aerodynamic forces, response amplitudes, and eigenvalues (which determine the flutter frequency and damping). In comparison with the TURBO-AE aeroelastic-analysis code, which solves the equations in the time domain, LINFLUX-AE is 6 to 7 times faster.

Trudell, J. J.↗
NASA NTRS1998

Evaluation of Aeroservoelastic Effects on Flutter

This report presents work performed by The Boeing Company to satisfy the deliverable "Evaluation of aeroservoelastic Effects on Symmetric Flutter" for Subtask 7 of Reference 1. The objective of this report is to incorporate the improved methods for studying the effects of a closed-loop control system on the aeroservoelastic behavior of the airplane planned under NASA HSR technical Integration Task 20 work. Also, a preliminary evaluation of the existing pitch control laws on symmetric flutter of the TCA configuration was addressed."The goal is to develop an improved modeling methodology and perform design studies that account for the aero-structures-systems interaction effects.

Nagaraja, K. S.↗
NASA NTRS2012

A Cross-Validation Approach to Approximate Basis Function Selection of the Stall Flutter Response of a Rectangular Wing in a Wind Tunnel

The stall flutter response of a rectangular wing in a low speed wind tunnel is modelled using a nonlinear difference equation description. Static and dynamic tests are used to select a suitable model structure and basis function. Bifurcation criteria such as the Hopf condition and vibration amplitude variation with airspeed were used to ensure the model was representative of experimentally measured stall flutter phenomena. Dynamic test data were used to estimate model parameters and estimate an approximate basis function.

Kukreja, Sunil L.↗
NASA NTRS2017

Uncertainty Quantification of the FUN3D-Predicted NASA CRM Flutter Boundary

A nonintrusive point collocation method is used to propagate parametric uncertainties of the flexible Common Research Model, a generic transport configuration, through the unsteady aeroelastic CFD solver FUN3D. A range of random input variables are considered, including atmospheric flow variables, structural variables, and inertial (lumped mass) variables. UQ results are explored for a range of output metrics (with a focus on dynamic flutter stability), for both subsonic and transonic Mach numbers, for two different CFD mesh refinements. A particular focus is placed on computing failure probabilities: the probability that the wing will flutter within the flight envelope.

Stanford, Bret K.↗
NASA NTRS2017

Investigating the Transonic Flutter Boundary of the Benchmark Supercritical Wing

This paper builds on the computational aeroelastic results published previously and generated in support of the second Aeroelastic Prediction Workshop for the NASA Benchmark Supercritical Wing configuration. The computational results are obtained using FUN3D, an unstructured grid Reynolds-Averaged Navier-Stokes solver developed at the NASA Langley Research Center. The analysis results focus on understanding the dip in the transonic flutter boundary at a single Mach number (0.74), exploring an angle of attack range of 􀀀1 to 8 and dynamic pressures from wind off to beyond flutter onset. The rigid analysis results are examined for insights into the behavior of the aeroelastic system. Both static and dynamic aeroelastic simulation results are also examined.

Heeg, Jennifer↗
NASA NTRS2018

Laser Displacement Measurements of Fan Blades in Resonance and Flutter During the Boundary Layer Ingesting Inlet and Distortion-Tolerant Fan Test

NASA's Advanced Air Transport Technology Project is investigating boundary layer ingesting propulsors for future subsonic commercial aircraft to improve aircraft efficiency, thereby reducing fuel burn. To that end, a boundary layer ingesting inlet and distortion-tolerant fan stage was designed, fabricated, and tested within the 8' x 6' Supersonic Wind Tunnel at NASA Glenn Research Center. Because of the distortion in the air flow over the fan, the blades were designed to withstand a much higher aerodynamic forcing than for a typical clean flow. The blade response for several resonance modes were measured during start-up and shutdown, as well as at near 85% design speed. Flutter in the first bending mode was also observed in the fan at the design speed, at an off-design condition, although instabilities were difficult to instigate with this fan in general. Blade vibrations were monitored through twelve laser displacement probes that were placed around the inner circumference of the casing, at the blade leading and trailing edges. These probes captured the movement of all the blades during the entire test. Results are presented for various resonance mode amplitudes, frequencies and damping, as well as flutter amplitudes and frequency. Benefits and disadvantages of laser displacement probe measurements versus strain gage measurements are discussed.

NSMS↗
NASA NTRS2018

Active Flutter Suppression Controllers Derived from Linear and Nonlinear Aerodynamics: Application to a Transport Aircraft Model

Active flutter suppression has been demonstrated in simulation by many researchers, generally using methods based on linear aerodynamics and often with simplistic geometries. In this paper, active flutter suppression is demonstrated in a simulation using a Navier-Stokes aerodynamics code, FUN3D (Fully Unstructured Navier-Stokes Three-Dimensional), and a realistic transport aircraft configuration. This is accomplished using simple observer-feedback controllers derived from linear aeroelastic models, including reduced order models built via FUN3D data. The development of these reduced order models is described here. It is shown that controllers derived from reduced order models of the nonlinear aerodynamics outperform controllers based on linear aerodynamics.

Waite, Josiah M.↗
NASA NTRS2019

Lessons from Modeling Flexible Aircraft for Active Flutter Suppression

These slides describe a method and technology of modeling flexible aircraft for active control of structural dynamics. Objective: Generate models useful for the design and evaluation of control laws for active structural control and flutter suppression that are able to accurately predict body freedom flutter.

Lessons Learned↗
NASA NTRS2019

Predicting Transonic Flutter Using Nonlinear Computational Simulations

This paper builds on the computational aeroelastic results published previously and generated in support of the second Aeroelastic Prediction Workshop for the NASA Benchmark Supercritical Wing configuration. The computational results are obtained using FUN3D, an unstructured flow solver developed at the NASA Langley Research Center. Flutter analyses are performed using various aerodynamic models including linear doublet lattice, Euler solutions, Reynolds averaged Navier-Stokes and Delayed Detached Eddy Simulations. The analyses are performed across the transonic Mach range for a range in angle of attack from 0° to 5°, with focus on identifying the transonic dips in the flutter onset boundaries.

Heeg, Jennifer↗
NASA NTRS2020

Flutter Analysis with Stabilized Finite Elements Based on the Linearized Frequency-Domain Approach

When designing and certifying aircraft, engineers must take into consideration aeroelastic effects such as flutter. Design and certification of a vehicle may require analysis of thousands of aeroelastic responses. Standard tools in the aerospace industry are based on linear aerodynamic models such as the doublet-lattice method, but these methods can be nonconservative in certain situations such as in the transonic regime. While computational fluid dynamics (CFD) is a higher fidelity alternative, the time-marching approach has a drastically increased computational cost compared to the linear aerodynamic methods. By taking advantage of the periodic nature of flutter, frequency-domain methods offer a more efficient alternative to time-marching CFD. In this work, a linearized frequency-domain method is implemented and verified in the stabilized finite-element solver in FUN3D. The linearized frequency-domain method is demonstrated and compared to other methods for traditional benchmark cases for computational aeroelasticity: the AGARD 445.6 wing, the Benchmark Supercritical Wing, and the Benchmark NACA 0012Wing.

Kevin E Jacobson↗
NASA NTRSDate not supplied

Flutter Analysis of the Transonic Truss-Braced Wing Aircraft Using Transonic Correction

This paper describes a flutter analysis method for the Transonic Truss-Braced Wing aircraft using a vortex lattice method coupled to an unsteady transonic correction method to account for unsteady aerodynamics in transonic flow. A steady-state vortex-lattice model of the Truss-Braced Wing aircraft is developed using vortex lattice code VSPAERO. A transonic and viscous flow correction method is implemented in the VSPAERO model to account for steady-state transonic and viscous flow effects using transonic small disturbance code TSFOIL coupled to an in-house integral boundary layer code. In addition, a wing-strut interference correction method is developed to account for the transonic interference aerodynamics in the strut juncture region using high-fidelity CFD code FUN3D. A structural dynamic finite-element model of the Truss-Braced Wing aircraft is developed using BEAM3D in-house finite-element code and is coupled to the VSPAERO. The BEAM3D model includes a geometric nonlinearity due to the tension in the strut which causes a deflection-dependent nonlinear stiffness. An unsteady transonic correction method is developed to better capture the unsteady aerodynamics in transonic flow. The unsteady transonic correction method makes use of the Theodorsen’s theory to account for the amplitude and phase shift of the unsteady lift coefficient in transonic flow. A preliminary flutter analysis of the Truss-Braced Wing aircraft is conduct to illustrate the unsteady transonic correction approach.

Nhan Nguyen↗
NASA NTRSDate not supplied

Atmospheric Boundary-Layer and Flutter Computations Using CFD Model of the Transonic Dynamics Tunnel

This paper presents two Computational Fluid Dynamics (CFD) models of the flow in the NASA Langley Research Center Transonic Dynamics Tunnel (TDT). The TDT is a continuous-flow, closed circuit wind tunnel with a 16- by 16-foot slotted test section with cropped corners. The tunnel was originally built as the 19-ft Pressure Tunnel in 1938, but it was converted to the current transonic tunnel in the 1950s, with capabilities to use either air or heavy gas as the test medium. The first computational model describes the generation of an atmospheric-boundary-layer (ABL) profile inside the tunnel. An ABL, which includes both a wind profile and turbulence content, is one of the aerodynamic characteristics affecting the occurrence of wind-induced oscillations for a launch vehicle sitting on a pad. The challenging part of this analysis was modeling the turbulent flow inside the tunnel. This is due to the special ABL-generating hardware that was installed at the entrance of the TDT test section in order to change the downstream velocity profile and to introduce velocity fluctuations into the flow. The second CFD model builds on the computational aeroelastic results that were generated in support of the second Aeroelastic Prediction Workshop (AePW) for the NASA Benchmark Supercritical Wing (BSCW) configuration. During the AePW, the wing-only configuration (classical free-air model) was analyzed. In the current study, the flutter computations were conducted on the configuration as it was mounted in the TDT during the experiment. This includes the wing attached to the splitter plate that was attached to the wind-tunnel walls. The preliminary results show that the wind-tunnel walls marginally affect flutter prediction.

Pawel Chwalowski↗
NASA NTRSDate not supplied

Multiscale Mesh Adaptation for Transonic Aeroelastic Flutter Problems

This work applies multiscale mesh adaptation with refine to reduce spatial discretization error of aeroelastic computational fluid dynamics (CFD) simulations. Benchmark flutter models, such as the pitch and plunge NACA64A-010 airfoil and the benchmark supercritical wing, are studied with both a linearized frequency-domain solver and time-marching CFD coupled to a modal structural solver in FUN3D. The undeformed NASA Common Research Model (CRM), an aeroelastic jig shape variant of the CRM, is also studied with the linearized frequency-domain approach. For these cases, the adaptation process converges to comparable flutter predictions to hand-generated meshes but with smaller node counts. However the additional disciplines of the linearized frequency-domain analysis, the mesh deformation, and the unsteady finite-volume solver create robustness challenges that need to be addressed before it can be applied as a fully automated process for complex transonic aeroelastic problems. In particular, negative volumes are observed to be an issue for FUN3D’s linear elasticity mesh deformation solver when moving the adapted meshes.

Aeroelasticity↗
NASA NTRSDate not supplied

Progress on Transonic Flutter and Shock Buffet Computationsin Support of the Third Aeroelastic Prediction Workshop

This paper reports on the progress of the NASA Langley team contributions to the third Aeroelastic Pre-diction Workshop’s (AePW-3) High Angle Working Group (HAWG). The primary objectives of HAWG is to predict the fluter dynamic pressure of the NASA Benchmark Supercritical Wing (BSCW) configuration at Mach 0.8 and 5◦angle of attack. The secondary objective is to determine if a shock-buffet onset is present at or near that flow condition. The computational results are obtained using FUN3D, an unstructured grid Reynolds-averaged Navier-Stokes solver developed at the NASA Langley Research Center. The preliminary analysis results show a computationally-obtained flutter dynamic pressure of approximately 120 psf. Initial results describing unforced BSCW unsteady flow environment at flutter condition are also presented.

Pawel Chwalowski↗
NASA NTRSDate not supplied

Fixed- and Free-Mode Flutter Derivatives during Aeroelastic Optimization

Aeroelastic flutter design derivatives may be approximated by ignoring the sensitivity of the structural mode shapes. This “fixed-mode” derivative is less-expensive to compute than the exact “free-mode” derivative (which accommodates the mode shape dependency), but also may provide inaccurate sensitivities for optimization. This work formulates both fixed- and free-mode flutter derivatives, and demonstrates the conditions under which they are equivalent. Two different types of fixed-mode derivatives are also developed in this work. Using a cantilevered-plate demonstration problem with both shape and sizing design parameters, the accuracy of the free-mode derivative and the two fixed-mode derivatives, are all demonstrated via gradient-based optimization.

Bret K. Stanford↗